Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces

Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces

We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks...

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Bibliographic Details
Main Authors: Santiago Hernández-Orozco, Hector Zenil, Jürgen Riedel, Adam Uccello, Narsis A. Kiani, Jesper Tegnér
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
algorithmic causality
generative mechanisms
program synthesis
non-differentiable machine learning
explainable AI
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2020.567356/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2020.567356/full

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